{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Bar Charts](07.02-Bar-Charts.ipynb) | [Contents](Index.ipynb) | [2D Plots](07.04-2D-Plots.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 饼状图\n",
    "\n",
    "首先，我们通过假设一些降雨，生成三个变量，径流、补给和蒸发。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "rainfall =1000\n",
    "runoff = 0.5*np.random.uniform()*rainfall\n",
    "recharge = 0.2*np.random.uniform()*rainfall\n",
    "evapotranspiration = rainfall - runoff - recharge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们根据默认大小修改图形大小，使其成为正方形。然后，我们通过指定`plt.axis`来定义饼状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\laihetao\\AppData\\Local\\conda\\conda\\envs\\DataProcess\\lib\\site-packages\\matplotlib\\axes\\_base.py:2961: UserWarning: Attempting to set identical left==right results\n",
      "in singular transformations; automatically expanding.\n",
      "left=0.2, right=0.2\n",
      "  'left=%s, right=%s') % (left, right))\n",
      "C:\\Users\\laihetao\\AppData\\Local\\conda\\conda\\envs\\DataProcess\\lib\\site-packages\\matplotlib\\axes\\_base.py:3285: UserWarning: Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=0.8, top=0.8\n",
      "  'bottom=%s, top=%s') % (bottom, top))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2, 0.2, 0.8, 0.8]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis([0.2,0.2,0.8,0.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们列出变量的列表和变量名称的元组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = 'Runoff','Recharge','Evapotranspiration'\n",
    "fracs = [runoff,recharge,evapotranspiration]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以使用爆炸(explode)来突出一些变量，在`Recharge`这种情况下。爆炸的数量由其参数控制。`autopct`用于定义饼状图内数字的格式。图7.3显示了饼状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21cc23b1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explode = [0,0.1,0]\n",
    "plt.pie(fracs,explode=explode,labels=labels,autopct='%1.1f%%',shadow=True)\n",
    "plt.title('Annual water balance',bbox={'facecolor':'0.6','pad':10})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图7.3:图形显示了一个饼状图例子</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
